Project Summary Individual genotypes influence disease incidence and severity. Personalized medicine seeks to use genotype information to optimize an individual's chances of achieving or maintaining health. Karyotyping, the practice of visually examining and recording chromosomal abnormalities, is one of the earliest and most common genotyping techniques; it is part of the standard-of-care for hematologic malignancies. As a result, large karyotype databases are available. For example, the publicly available Mitelman database contains >65,000 karyotypes. However, current clinical use of karyotype data is limited to patterns that are visually apparent to cytogeneticists when scanning textual representations of karyotypes. Cytogeneticists record karyotypes in a standardized notation, the International System for Human Cytogenetic Nomenclature, which is human-readable but often difficult to analyze. As a result, clinically relevant patterns hidden within long, complex karyotypes remain undiscovered. We have developed a computational tool, CytoGenetic Pattern Sleuth (CytoGPS), which translates ?raw? karyotypes into a computable form that records loss, gain, or fusion (LGF) at the resolution of cytogenetic bands. CytoGPS enables the secondary use of karyotype data by facilitating the application of modern data mining tools. The long-term goal of this project is to combine karyotype, genotype, and phenotype data to enable personalized cancer diagnostics and therapeutics. Our main hypothesis is that CytoGPS can address important, clinically actionable questions. Using Chronic Lymphocytic Leukemia (CLL) as a model disease, we will analyze data on 1827 patients from an OSU research data repository to test this hypothesis with the following Specific Aims: Aim 1: To analyze cytogenetic data from CLL patients using CytoGPS in order to discover novel, previously unrecognized, recurrent abnormalities or patterns of abnormalities in stimulated cells from patients with CLL. We will also test whether patterns of abnormalities are associated with disease onset or progression. Aim 2: To use CytoGPS to construct, verify, and validate predictive models of CLL patient response to the two most common therapies: Ibrutinib, or the combination therapy Fludarabine, Cytoxan, Rituximab (FCR).